data <- read.csv("data.csv", encoding="ISO-8859-1")

head(data)

dim(data)

length(unique(data$CustomerID))

head(data[data$Quantity < 0, ])

str(data)

'data.frame':	541909 obs. of  8 variables:
 $ InvoiceNo  : Factor w/ 25900 levels "536365","536366",..: 1 1 1 1 1 1 1 2 2 3 ...
 $ StockCode  : Factor w/ 4070 levels "10002","10080",..: 3538 2795 3045 2986 2985 1663 801 1548 1547 3306 ...
 $ Description: Factor w/ 4224 levels ""," 4 PURPLE FLOCK DINNER CANDLES",..: 4027 4035 932 1959 2980 3235 1573 1698 1695 259 ...
 $ Quantity   : int  6 6 8 6 6 2 6 6 6 32 ...
 $ InvoiceDate: Factor w/ 23260 levels "1/10/2011 10:04",..: 6839 6839 6839 6839 6839 6839 6839 6840 6840 6841 ...
 $ UnitPrice  : num  2.55 3.39 2.75 3.39 3.39 7.65 4.25 1.85 1.85 1.69 ...
 $ CustomerID : int  17850 17850 17850 17850 17850 17850 17850 17850 17850 13047 ...
 $ Country    : Factor w/ 38 levels "Australia","Austria",..: 36 36 36 36 36 36 36 36 36 36 ...

null_percentage <- colMeans(is.na(data)) * 100
null_percentage

colSums(is.na(data[, sapply(data, is.numeric)]))

colSums(is.na(data[, sapply(data, is.factor)]))

numeric_data <- data[, sapply(data, is.numeric)]

# Generate summary statistics for integer and numeric variables
t(summary(numeric_data))

                                                                        
   Quantity  Min.   :-80995.00   1st Qu.:     1.00   Median :     3.00  
  UnitPrice  Min.   :-11062.06   1st Qu.:     1.25   Median :     2.08  
  CustomerID Min.   :12346       1st Qu.:13953       Median :15152      
                                                                        
   Quantity  Mean   :     9.55   3rd Qu.:    10.00   Max.   : 80995.00  
  UnitPrice  Mean   :     4.61   3rd Qu.:     4.13   Max.   : 38970.00  
  CustomerID Mean   :15288       3rd Qu.:16791       Max.   :18287      
                             
   Quantity                  
  UnitPrice                  
  CustomerID NA's   :135080

categorical_data <- data[, sapply(data, is.factor)]

# Generate summary statistics for categorical variables
t(summary(categorical_data))

                                                                                   
  InvoiceNo                             573585 :  1114                             
  StockCode                             85123A :  2313                             
                            Description WHITE HANGING HEART T-LIGHT HOLDER:  2369  
          InvoiceDate                   10/31/2011 14:41:  1114                    
          Country                       United Kingdom:495478                      
                                                                                   
  InvoiceNo                             581219 :   749                             
  StockCode                             22423  :  2203                             
                            Description REGENCY CAKESTAND 3 TIER          :  2200  
          InvoiceDate                   12/8/2011 9:28  :   749                    
          Country                       Germany       :  9495                      
                                                                                   
  InvoiceNo                             581492 :   731                             
  StockCode                             85099B :  2159                             
                            Description JUMBO BAG RED RETROSPOT           :  2159  
          InvoiceDate                   12/9/2011 10:03 :   731                    
          Country                       France        :  8557                      
                                                                                   
  InvoiceNo                             580729 :   721                             
  StockCode                             47566  :  1727                             
                            Description PARTY BUNTING                     :  1727  
          InvoiceDate                   12/5/2011 17:24 :   721                    
          Country                       EIRE          :  8196                      
                                                                                   
  InvoiceNo                             558475 :   705                             
  StockCode                             20725  :  1639                             
                            Description LUNCH BAG RED RETROSPOT           :  1638  
          InvoiceDate                   6/29/2011 15:58 :   705                    
          Country                       Spain         :  2533                      
                                                                                   
  InvoiceNo                             579777 :   687                             
  StockCode                             84879  :  1502                             
                            Description ASSORTED COLOUR BIRD ORNAMENT     :  1501  
          InvoiceDate                   11/30/2011 15:13:   687                    
          Country                       Netherlands   :  2371                      
                                                                                   
  InvoiceNo                             (Other):537202                             
  StockCode                             (Other):530366                             
                            Description (Other)                           :530315  
          InvoiceDate                   (Other)         :537202                    
          Country                       (Other)       : 15279

country_proportions <- prop.table(table(data$Country))

# Format the proportions as decimals
formatted_proportions <- format(country_proportions, scientific = FALSE)

formatted_proportions

data$InvoiceNo <- as.character(data$InvoiceNo)
data$Description <- as.character(data$Description)
data$Description <- trimws(data$Description)

head(data)

str(data)

'data.frame':	541909 obs. of  8 variables:
 $ InvoiceNo  : chr  "536365" "536365" "536365" "536365" ...
 $ StockCode  : Factor w/ 4070 levels "10002","10080",..: 3538 2795 3045 2986 2985 1663 801 1548 1547 3306 ...
 $ Description: chr  "WHITE HANGING HEART T-LIGHT HOLDER" "WHITE METAL LANTERN" "CREAM CUPID HEARTS COAT HANGER" "KNITTED UNION FLAG HOT WATER BOTTLE" ...
 $ Quantity   : int  6 6 8 6 6 2 6 6 6 32 ...
 $ InvoiceDate: Factor w/ 23260 levels "1/10/2011 10:04",..: 6839 6839 6839 6839 6839 6839 6839 6840 6840 6841 ...
 $ UnitPrice  : num  2.55 3.39 2.75 3.39 3.39 7.65 4.25 1.85 1.85 1.69 ...
 $ CustomerID : int  17850 17850 17850 17850 17850 17850 17850 17850 17850 13047 ...
 $ Country    : Factor w/ 38 levels "Australia","Austria",..: 36 36 36 36 36 36 36 36 36 36 ...

data <- data[!is.na(data$CustomerID), ]

colMeans(is.na(data)) * 100

sum(duplicated(data))

duplicates <- duplicated(data)

# Extract duplicate rows
duplicate_rows <- data[duplicates, ]
duplicate_rows

data <- data[!duplicated(data), ]
# Reset row names
row.names(data) <- NULL

# Print the data frame after removing duplicates
head(data)

dim(data)

length(unique(data$CustomerID))

data$is_Cancelled <- startsWith(data$InvoiceNo, "C")

head(data)

prop.table(table(data$is_Cancelled))

     FALSE       TRUE 
0.97790859 0.02209141

format(prop.table(table(data$is_Cancelled)), scientific=FALSE)

cancelled_data <- data[data$is_Cancelled, ]

# Remove the "CustomerID" column
cancelled_data <- cancelled_data[, !names(cancelled_data) %in% "CustomerID"]

# Generate summary statistics for the filtered data frame
summary_data <- summary(cancelled_data)

# Transpose the summary statistics
summary_data_transposed <- t(summary_data)

# Print the transposed summary statistics
print(summary_data_transposed)

                                                                     
 InvoiceNo            Length:8872             Class :character       
  StockCode           22423  : 180            M      : 175           
Description           Length:8872             Class :character       
   Quantity           Min.   :-80995.00       1st Qu.:    -6.00      
          InvoiceDate 10/12/2011 16:17: 101   7/19/2011 12:26 :  57  
  UnitPrice           Min.   :    0.01        1st Qu.:    1.45       
          Country     United Kingdom:7501     Germany       : 453    
is_Cancelled          Mode:logical            TRUE:8872              
                                                                     
 InvoiceNo            Mode  :character                               
  StockCode           POST   :  97            22960  :  86           
Description           Mode  :character                               
   Quantity           Median :    -2.00       Mean   :   -30.77      
          InvoiceDate 3/31/2011 11:58 :  45   7/21/2011 13:00 :  40  
  UnitPrice           Median :    2.95        Mean   :   18.90       
          Country     EIRE          : 247     France        : 148    
is_Cancelled                                                         
                                                                     
 InvoiceNo                                                           
  StockCode           D      :  77            22720  :  72           
Description                                                          
   Quantity           3rd Qu.:    -1.00       Max.   :    -1.00      
          InvoiceDate 10/12/2011 13:15:  39   10/6/2011 19:51 :  36  
  UnitPrice           3rd Qu.:    4.95        Max.   :38970.00       
          Country     USA           : 112     Australia     :  74    
is_Cancelled                                                         
                                             
 InvoiceNo                                   
  StockCode           (Other):8185           
Description                                  
   Quantity                                  
          InvoiceDate (Other)         :8554  
  UnitPrice                                  
          Country     (Other)       : 337    
is_Cancelled

library(ggplot2)

# Calculate the interquartile range (IQR) for the Quantity column
Q1 <- quantile(data$Quantity, 0.02)
Q3 <- quantile(data$Quantity, 0.98)
IQR <- Q3 - Q1

# Define the lower and upper bounds for outliers
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR

# Filter out rows with Quantity values outside the bounds
data_filtered <- data[data$Quantity >= lower_bound & data$Quantity <= upper_bound, ]

# Create a histogram of Quantity with the filtered data
ggplot(data_filtered, aes(x = Quantity)) +
  geom_histogram(binwidth = 10, fill = "skyblue", color = "black") +
  labs(title = "Distribution of Quantity (Filtered)", x = "Quantity", y = "Frequency")

library(ggplot2)

# Create a box plot of the "Quantity" column
ggplot(data, aes(y = Quantity)) +
  geom_boxplot(notch = TRUE) +
  labs(title = "Box Plot of Quantity")

# Filter rows where is_Cancelled is FALSE
filtered_data <- data[!data$is_Cancelled, ]

# Remove the "CustomerID" column
filtered_data <- subset(filtered_data, select = -CustomerID)

# Transpose the summary statistics
summary_data_transposed <- t(summary(filtered_data))

# Print the transposed summary statistics
print(summary_data_transposed)

                                                                         
 InvoiceNo            Length:392732             Class :character         
  StockCode           85123A :  2023            22423  :  1714           
Description           Length:392732             Class :character         
   Quantity           Min.   :    1.00          1st Qu.:    2.00         
          InvoiceDate 11/14/2011 15:27:   542   11/28/2011 15:54:   533  
  UnitPrice           Min.   :   0.000          1st Qu.:   1.250         
          Country     United Kingdom:349227     Germany       :  9027    
is_Cancelled          Mode :logical             FALSE:392732             
                                                                         
 InvoiceNo            Mode  :character                                   
  StockCode           85099B :  1615            84879  :  1395           
Description           Mode  :character                                   
   Quantity           Median :    6.00          Mean   :   13.15         
          InvoiceDate 12/5/2011 17:17 :   529   11/23/2011 13:39:   443  
  UnitPrice           Median :   1.950          Mean   :   3.126         
          Country     France        :  8327     EIRE          :  7228    
is_Cancelled                                                             
                                                                         
 InvoiceNo                                                               
  StockCode           47566  :  1390            20725  :  1304           
Description                                                              
   Quantity           3rd Qu.:   12.00          Max.   :80995.00         
          InvoiceDate 10/31/2011 14:09:   435   9/21/2011 14:40 :   421  
  UnitPrice           3rd Qu.:   3.750          Max.   :8142.750         
          Country     Spain         :  2480     Netherlands   :  2363    
is_Cancelled                                                             
                                               
 InvoiceNo                                     
  StockCode           (Other):383291           
Description                                    
   Quantity                                    
          InvoiceDate (Other)         :389829  
  UnitPrice                                    
          Country     (Other)       : 14080    
is_Cancelled

non_cancelled_data <- subset(data, !is_Cancelled)

# Create a box plot of the "Quantity" column
ggplot(non_cancelled_data, aes(y = Quantity)) +
  geom_boxplot(notch = TRUE) +
  labs(title = "Box Plot of Quantity")

# Load the ggplot2 and scales packages
library(ggplot2)
library(scales)

# Filter the data frame to include only non-cancelled orders
non_cancelled_data <- subset(data, !is_Cancelled)

# Create a box plot of the "Quantity" column with logarithmic scale on the y-axis
ggplot(non_cancelled_data, aes(y = Quantity)) +
  geom_boxplot(notch = TRUE) +
  scale_y_continuous(trans = "log10") +
  labs(title = "Box Plot of Quantity")

length(unique(data$StockCode))

# Load the stringr package
library(stringr)

# Create a new column "len_StockCode" to store the length of trimmed "StockCode" values
data$len_StockCode <- nchar(str_trim(data$StockCode))

# Print the modified data frame
head(data)

format(prop.table(table(data$len_StockCode)), scientific=FALSE)

num_unique_stockcodes <- length(unique(data[data$len_StockCode == 5, "StockCode"]))

# Print the number of unique stock codes
print(num_unique_stockcodes)

[1] 2798

filtered_data <- data[data$len_StockCode < 5, ]

# Print the filtered data frame
filtered_data

dim(filtered_data)

filtered_data <- subset(data, len_StockCode < 5)

# Calculate the frequency of each unique value in the "StockCode" column and normalize
value_counts <- format(prop.table(table(filtered_data$StockCode)), scientific=FALSE)

value_counts_dict <- as.list(value_counts)

# Filter out values whose counts are greater than 0
filtered_counts_dict <- value_counts_dict[value_counts_dict > 0]

# Print the filtered dictionary
print(filtered_counts_dict)

$C2
[1] "0.070415134"

$CRUK
[1] "0.008407777"

$D
[1] "0.040462428"

$DOT
[1] "0.008407777"

$M
[1] "0.241723594"

$PADS
[1] "0.002101944"

$POST
[1] "0.628481345"

filtered_data <- subset(data, len_StockCode > 7)

# Calculate the frequency of each unique value in the "StockCode" column and normalize
value_counts <- table(filtered_data$StockCode)
value_counts_dict <- as.list(value_counts)

# Filter out values whose counts are greater than 0
filtered_counts_dict <- value_counts_dict[value_counts_dict > 0]

# Print the filtered dictionary
print(filtered_counts_dict)

$`BANK CHARGES`
[1] 12

filtered_data <- subset(data, len_StockCode >= 5 & len_StockCode < 8)

# Calculate the frequency of each unique value in the "StockCode" column and normalize
print(length(unique(filtered_data$StockCode)))

[1] 3676

dim(filtered_data)

dim(data)

# Total number of records
total_records <- nrow(data)

# Number of records with anomalous stock codes
anomalous_records <- total_records - nrow(filtered_data)

# Calculate the percentage of records with anomalous stock codes
percentage_anomalous <- (anomalous_records / total_records) * 100

# Print the percentage
print(paste("The percentage of records with anomalous stock codes in the dataset is:", round(percentage_anomalous, 2), "%"))

[1] "The percentage of records with anomalous stock codes in the dataset is: 0.48 %"

data <- data[data$len_StockCode >= 5 & data$len_StockCode < 8, ]

dim(data)

boxplot(data$UnitPrice, notch = TRUE, main = "Box Plot of UnitPrice", ylab = "UnitPrice")

filtered_data <- subset(data, UnitPrice > 0)
# Disable scientific notation for numeric output
options(scipen = 999)
# Create a box plot of the filtered "UnitPrice" column with logarithmic scale on y-axis
boxplot(filtered_data$UnitPrice, notch = TRUE, main = "Box Plot of UnitPrice", ylab = "UnitPrice", log = "y")

table(data$is_Cancelled)

 FALSE   TRUE 
391183   8506

dim(data)

summary(data$UnitPrice)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   1.250   1.950   2.908   3.750 649.500

zero_price_data <- data[data$UnitPrice == 0, ]

# Summary statistics for the "Quantity" column of the filtered data
summary(zero_price_data$Quantity)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    1.0     2.0    11.0   420.5    36.0 12540.0

zero_price_data <- data[data$UnitPrice == 0, ]

# Display the filtered data
head(zero_price_data)

# Filter the data to remove records with unit price of zero
data <- data[data$UnitPrice > 0, ]

str(data$InvoiceDate)

 Factor w/ 23260 levels "1/10/2011 10:04",..: 6839 6839 6839 6839 6839 6839 6839 6840 6840 6841 ...

copied_df <- data.frame(data)

library(dplyr)

# Convert InvoiceDate to datetime with specified format
data$InvoiceDate <- as.POSIXct(data$InvoiceDate, format = "%m/%d/%Y %H:%M")

# Set InvoiceDate as index
data <- data %>% 
  arrange(InvoiceDate) %>%  # Ensure data is sorted by InvoiceDate
  mutate(InvoiceDate = as.Date(InvoiceDate))  # Convert to Date type

# Aggregate sales data
data$Sales <- data$Quantity * data$UnitPrice
daily_sales <- data %>% 
  group_by(InvoiceDate) %>% 
  summarize(Sales = sum(Sales))

str(data$InvoiceDate)

 Date[1:399656], format: "2010-12-01" "2010-12-01" "2010-12-01" "2010-12-01" "2010-12-01" ...

head(data)

dim(data)

daily_sales

# Load required packages
library(ggplot2)
library(tibble)

# Convert daily_sales dataframe to tibble
daily_sales <- as_tibble(daily_sales)

# Create time series plot using ggplot2
ggplot(daily_sales, aes(x = InvoiceDate, y = Sales)) +
  geom_line() +
  labs(title = "Daily Sales Over Time", x = "Date", y = "Sales")

Registered S3 methods overwritten by 'ggplot2':
  method         from 
  [.quosures     rlang
  c.quosures     rlang
  print.quosures rlang

library(ggplot2)

# Aggregate sales by country
sales_by_country <- aggregate(Sales ~ Country, data = data, FUN = sum)

# Create bar plot
ggplot(sales_by_country, aes(x = Country, y = Sales)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Total Sales by Country", x = "Country", y = "Total Sales") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

# Create scatter plot
ggplot(data, aes(x = UnitPrice, y = Quantity)) +
  geom_point() +
  labs(title = "Scatter Plot of Quantity vs. Unit Price", x = "Unit Price", y = "Quantity")

library(lubridate)
library(dplyr)

# Load required libraries
# Convert InvoiceDate to datetime with specified format
data$InvoiceDate <- as.POSIXct(data$InvoiceDate, format = "%m/%d/%Y %H:%M")

# Set InvoiceDate as index and convert to Date type
data <- data %>% 
  arrange(InvoiceDate) %>%  # Ensure data is sorted by InvoiceDate
  mutate(InvoiceDate = as.Date(InvoiceDate))

# Calculate sales
data$Sales <- data$Quantity * data$UnitPrice

# Aggregate sales data by month
monthly_sales <- data %>% 
  group_by(YearMonth = format(InvoiceDate, "%Y-%m")) %>%  # Extract year-month from date
  summarize(Sales = sum(Sales))

monthly_sales

# Plot Monthly Sales
ggplot(monthly_sales, aes(x = YearMonth, y = Sales)) +
  geom_point() +
  labs(title = "Monthly Sales Over Time", x = "Month", y = "Sales")

library(lubridate)
library(dplyr)
library(plotly)

# Aggregate sales data by month
monthly_sales <- data %>%
  group_by(YearMonth = format(InvoiceDate, "%Y-%m")) %>%
  summarise(Sales = sum(Sales))

# Plot Monthly Sales
plotly::plot_ly(monthly_sales, x = ~YearMonth, y = ~Sales, type = "scatter", mode = "lines") %>%
  plotly::layout(title = "Monthly Sales Over Time", xaxis = list(title = "Month"), yaxis = list(title = "Sales"))

# Load required libraries
library(lubridate)
library(dplyr)
library(plotly)

# Aggregate sales data by week
weekly_sales <- data %>%
  group_by(Week = format(InvoiceDate, "%Y-%U")) %>%
  summarise(Sales = sum(Sales))

# Plot Weekly Sales
plotly::plot_ly(weekly_sales, x = ~Week, y = ~Sales, type = "scatter", mode = "lines") %>%
  plotly::layout(title = "Weekly Sales Over Time", xaxis = list(title = "Week"), yaxis = list(title = "Sales"))

head(data)

# Calculate the number of unique values in the "StockCode" column
unique_stock_codes <- data %>%
  distinct(StockCode) %>%
  nrow()

# Print the number of unique stock codes
print(unique_stock_codes)

[1] 3676

# Calculate the number of unique values in the "CustomerID" column
unique_customer_ids <- data %>%
  distinct(CustomerID) %>%
  nrow()

# Print the number of unique customer IDs
print(unique_customer_ids)

[1] 4362

head(data)

# Step 1: Calculate Total Value per Key
value_per_key <- data %>%
  group_by(CustomerID) %>%
  summarise(Total_Value = sum(Sales))

head(value_per_key)

value_per_key_sorted <- value_per_key %>%
  arrange(desc(Total_Value))

head(value_per_key_sorted)

value_per_key_sorted$Cumulative_Percentage <- cumsum(value_per_key_sorted$Total_Value) / sum(value_per_key_sorted$Total_Value) * 100

head(value_per_key_sorted)

# Calculate the percentage of customers contributing to 80% of sales
perc <- round(sum(value_per_key_sorted$Cumulative_Percentage < 80) / nrow(value_per_key_sorted) * 100)

# Print the result
print(paste0(perc, "% of Customers contribute to 80% of sales."))

[1] "27% of Customers contribute to 80% of sales."

# Calculate the number of rows where Cumulative_Percentage < 80
num_rows <- nrow(value_per_key_sorted[value_per_key_sorted$Cumulative_Percentage < 80, ])

# Print the result
print(num_rows)

[1] 1163

# Step 1: Calculate Total Value per Key
value_per_key_stock <- data %>%
  group_by(StockCode) %>%
  summarise(Total_Value = sum(Sales))

value_per_key_stock_sorted <- value_per_key_stock %>%
  arrange(desc(Total_Value))

value_per_key_stock_sorted$Cumulative_Percentage <- cumsum(value_per_key_stock_sorted$Total_Value) / sum(value_per_key_stock_sorted$Total_Value) * 100

head(value_per_key_stock_sorted)

# Calculate the percentage of products contributing to 80% of sales
perc <- round(sum(value_per_key_stock_sorted$Cumulative_Percentage < 80) / nrow(value_per_key_stock_sorted) * 100)

# Print the result
print(paste0(perc, "% of Products contribute to 80% of sales."))

[1] "22% of Products contribute to 80% of sales."

# Calculate the number of rows where Cumulative_Percentage < 80
num_rows <- nrow(value_per_key_stock_sorted[value_per_key_stock_sorted$Cumulative_Percentage < 80, ])

# Print the result
print(num_rows)

[1] 800

# Load required libraries
library(arules)
library(dplyr)
library(tidyr)

# Step 1: Group data by InvoiceNo and create a list of items purchased in each transaction
transactions <- data %>%
  group_by(InvoiceNo) %>%
  summarize(Items = list(StockCode))

# Step 2: Remove duplicated items in transactions
transactions$Items <- lapply(transactions$Items, unique)

# Step 3: Convert transactions into a transaction object
transactions <- as(transactions$Items, "transactions")

# Step 4: Mine frequent itemsets
frequent_itemsets <- apriori(transactions, parameter = list(support = 0.01))

# Step 5: Find association rules
rules <- apriori(transactions, parameter = list(support = 0.01, confidence = 0.5))

# Step 6: Sort and display the top 10 association rules by lift
rules_df <- as.data.frame(inspect(rules))
top_10_rules <- rules_df[order(-rules_df$lift), ][1:10, ]

# Display the top 10 association rules
print(top_10_rules)

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen
        0.8    0.1    1 none FALSE            TRUE       5    0.01      1
 maxlen target   ext
     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 217 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[3676 item(s), 21785 transaction(s)] done [0.25s].
sorting and recoding items ... [532 item(s)] done [0.01s].
creating transaction tree ... done [0.01s].
checking subsets of size 1 2 3 4 done [0.03s].
writing ... [10 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen
        0.5    0.1    1 none FALSE            TRUE       5    0.01      1
 maxlen target   ext
     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 217 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[3676 item(s), 21785 transaction(s)] done [0.26s].
sorting and recoding items ... [532 item(s)] done [0.00s].
creating transaction tree ... done [0.02s].
checking subsets of size 1 2 3 4 done [0.03s].
writing ... [196 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
      lhs                    rhs      support    confidence lift      count
[1]   {21086}             => {21094}  0.01083314 0.8280702  55.506181 236  
[2]   {21094}             => {21086}  0.01083314 0.7261538  55.506181 236  
[3]   {84997C}            => {84997D} 0.01051182 0.7387097  37.688034 229  
[4]   {84997D}            => {84997C} 0.01051182 0.5362998  37.688034 229  
[5]   {23254}             => {23256}  0.01005279 0.7711268  50.599387 219  
[6]   {23256}             => {23254}  0.01005279 0.6596386  50.599387 219  
[7]   {23171}             => {23170}  0.01064953 0.8436364  54.054759 232  
[8]   {23170}             => {23171}  0.01064953 0.6823529  54.054759 232  
[9]   {47590B}            => {47590A} 0.01175120 0.7052342  42.914878 256  
[10]  {47590A}            => {47590B} 0.01175120 0.7150838  42.914878 256  
[11]  {22725}             => {22727}  0.01055772 0.6865672  16.490480 230  
[12]  {21136}             => {84879}  0.01175120 0.7231638  11.374819 256  
[13]  {22729}             => {22726}  0.01028230 0.5957447  16.042395 224  
[14]  {22729}             => {22727}  0.01165940 0.6755319  16.225428 254  
[15]  {23295}             => {23293}  0.01133808 0.6785714  31.452508 247  
[16]  {23293}             => {23295}  0.01133808 0.5255319  31.452508 247  
[17]  {22579}             => {22578}  0.01042001 0.8194946  40.946536 227  
[18]  {22578}             => {22579}  0.01042001 0.5206422  40.946536 227  
[19]  {21094}             => {21080}  0.01051182 0.7046154  20.912869 229  
[20]  {82581}             => {82580}  0.01042001 0.7394137  41.515791 227  
[21]  {82580}             => {82581}  0.01042001 0.5850515  41.515791 227  
[22]  {23243}             => {22720}  0.01009869 0.5250597   9.391153 220  
[23]  {22745}             => {22748}  0.01170530 0.7993730  49.897827 255  
[24]  {22748}             => {22745}  0.01170530 0.7306590  49.897827 255  
[25]  {22625}             => {22624}  0.01239385 0.5793991  20.067107 270  
[26]  {23343}             => {23344}  0.01133808 0.6301020  23.148015 247  
[27]  {22749}             => {22750}  0.01092495 0.5935162  34.025660 238  
[28]  {22750}             => {22749}  0.01092495 0.6263158  34.025660 238  
[29]  {22730}             => {22726}  0.01266927 0.5750000  15.483776 276  
[30]  {22730}             => {22727}  0.01459720 0.6625000  15.912417 318  
[31]  {84596F}            => {84596B} 0.01037411 0.7820069  47.060831 226  
[32]  {84596B}            => {84596F} 0.01037411 0.6243094  47.060831 226  
[33]  {22617}             => {22138}  0.01432178 0.7107062  17.574045 312  
[34]  {23200}             => {23199}  0.01450539 0.6638655  19.570110 316  
[35]  {23200}             => {23202}  0.01106266 0.5063025  14.177121 241  
[36]  {23200}             => {23203}  0.01248566 0.5714286  11.347832 272  
[37]  {22328}             => {22326}  0.01225614 0.5644820  20.093531 267  
[38]  {82483}             => {82486}  0.01294469 0.5136612  19.293292 282  
[39]  {21231}             => {21232}  0.01188891 0.6348039  21.641946 259  
[40]  {21930}             => {85099B} 0.01142988 0.5253165   6.965319 249  
[41]  {22385}             => {85099B} 0.01234795 0.5592516   7.415274 269  
[42]  {22866}             => {22867}  0.01106266 0.5073684  23.027127 241  
[43]  {22867}             => {22866}  0.01106266 0.5020833  23.027127 241  
[44]  {22866}             => {22865}  0.01289878 0.5915789  22.105570 281  
[45]  {20712}             => {85099B} 0.01216433 0.5289421   7.013393 265  
[46]  {22114}             => {22112}  0.01198072 0.5038610  15.525618 261  
[47]  {22698}             => {22697}  0.02162038 0.7969543  23.685743 471  
[48]  {22697}             => {22698}  0.02162038 0.6425648  23.685743 471  
[49]  {22698}             => {22699}  0.02070232 0.7631134  19.885675 451  
[50]  {22699}             => {22698}  0.02070232 0.5394737  19.885675 451  
[51]  {22698}             => {22423}  0.01464310 0.5397631   6.244684 319  
[52]  {21928}             => {21929}  0.01303649 0.5144928  18.254438 284  
[53]  {21928}             => {85099B} 0.01491852 0.5887681   7.806642 325  
[54]  {22835}             => {22112}  0.01331191 0.5686275  17.521286 290  
[55]  {22804}             => {85123A} 0.01400046 0.7314149   7.888056 305  
[56]  {22578}             => {22577}  0.01468901 0.7339450  34.384927 320  
[57]  {22577}             => {22578}  0.01468901 0.6881720  34.384927 320  
[58]  {23322}             => {23321}  0.01294469 0.5412668  20.122009 282  
[59]  {22867}             => {22865}  0.01230204 0.5583333  20.863279 268  
[60]  {85099C}            => {85099B} 0.01698416 0.5580694   7.399599 370  
[61]  {20719}             => {20724}  0.01331191 0.5380334  15.524579 290  
[62]  {20971}             => {20972}  0.01083314 0.5119306  20.767985 236  
[63]  {21791}             => {21790}  0.01147579 0.5122951  15.393584 250  
[64]  {21914}             => {21915}  0.01248566 0.5240848  20.065355 272  
[65]  {85099F}            => {85099B} 0.01937113 0.6326837   8.388931 422  
[66]  {22356}             => {20724}  0.01496443 0.6468254  18.663697 326  
[67]  {20723}             => {20724}  0.01377094 0.5952381  17.175181 300  
[68]  {23208}             => {23206}  0.01404636 0.5091514  12.633102 306  
[69]  {23208}             => {23209}  0.01464310 0.5307820  11.369800 319  
[70]  {21929}             => {85099B} 0.01537755 0.5456026   7.234299 335  
[71]  {22355}             => {20724}  0.01381685 0.5041876  14.547983 301  
[72]  {22728}             => {22726}  0.01579068 0.5468998  14.727087 344  
[73]  {22728}             => {22727}  0.01849897 0.6406995  15.388797 403  
[74]  {22697}             => {22699}  0.02556805 0.7598909  19.801701 557  
[75]  {22699}             => {22697}  0.02556805 0.6662679  19.801701 557  
[76]  {22697}             => {22423}  0.01758090 0.5225102   6.045080 383  
[77]  {22662}             => {22382}  0.01634152 0.5884298  12.806136 356  
[78]  {22662}             => {20725}  0.01418407 0.5107438   8.365830 309  
[79]  {82494L}            => {82482}  0.02175809 0.5738499  13.983579 474  
[80]  {82482}             => {82494L} 0.02175809 0.5302013  13.983579 474  
[81]  {22699}             => {22423}  0.01983016 0.5167464   5.978396 432  
[82]  {23202}             => {23203}  0.01973835 0.5526992  10.975891 430  
[83]  {22551}             => {22554}  0.01349552 0.5008518  18.430838 294  
[84]  {22386}             => {85099B} 0.02543034 0.6266968   8.309550 554  
[85]  {21931}             => {85099B} 0.02033509 0.5600506   7.425868 443  
[86]  {22630}             => {22629}  0.01946293 0.6784000  20.756944 424  
[87]  {22629}             => {22630}  0.01946293 0.5955056  20.756944 424  
[88]  {21975}             => {21212}  0.01230204 0.5037594  10.542170 268  
[89]  {22910}             => {22086}  0.02084003 0.6403385  14.090681 454  
[90]  {22726}             => {22727}  0.02460409 0.6625464  15.913531 536  
[91]  {22727}             => {22726}  0.02460409 0.5909592  15.913531 536  
[92]  {21733}             => {85123A} 0.02120725 0.6676301   7.200159 462  
[93]  {21977}             => {21212}  0.01551526 0.5007407  10.478998 338  
[94]  {23300}             => {23301}  0.02139087 0.7258567  20.616412 466  
[95]  {23301}             => {23300}  0.02139087 0.6075619  20.616412 466  
[96]  {84991}             => {21212}  0.01533165 0.5030120  10.526530 334  
[97]  {20726}             => {20725}  0.02015148 0.5232420   8.570546 439  
[98]  {22384}             => {20725}  0.02432867 0.5573081   9.128539 530  
[99]  {22697,22698}       => {22699}  0.01822355 0.8428875  21.964478 397  
[100] {22698,22699}       => {22697}  0.01822355 0.8802661  26.161796 397  
[101] {22697,22699}       => {22698}  0.01822355 0.7127469  26.272742 397  
[102] {22697,22698}       => {22423}  0.01257746 0.5817410   6.730338 274  
[103] {22423,22698}       => {22697}  0.01257746 0.8589342  25.527805 274  
[104] {22423,22697}       => {22698}  0.01257746 0.7154047  26.370713 274  
[105] {22698,22699}       => {22423}  0.01243975 0.6008869   6.951844 271  
[106] {22423,22698}       => {22699}  0.01243975 0.8495298  22.137567 271  
[107] {22423,22699}       => {22698}  0.01243975 0.6273148  23.123610 271  
[108] {22386,85099F}      => {85099B} 0.01069543 0.7925170  10.508206 233  
[109] {85099B,85099F}     => {22386}  0.01069543 0.5521327  13.606573 233  
[110] {22726,22728}       => {22727}  0.01230204 0.7790698  18.712277 268  
[111] {22727,22728}       => {22726}  0.01230204 0.6650124  17.907658 268  
[112] {22726,22727}       => {22728}  0.01230204 0.5000000  17.317170 268  
[113] {22697,22699}       => {22423}  0.01450539 0.5673250   6.563555 316  
[114] {22423,22697}       => {22699}  0.01450539 0.8250653  21.500056 316  
[115] {22423,22699}       => {22697}  0.01450539 0.7314815  21.739869 316  
[116] {23202,23203}       => {85099B} 0.01046592 0.5302326   7.030503 228  
[117] {23202,85099B}      => {23203}  0.01046592 0.6570605  13.048371 228  
[118] {21931,22386}       => {85099B} 0.01023640 0.7852113  10.411337 223  
[119] {21931,85099B}      => {22386}  0.01023640 0.5033860  12.405276 223  
[120] {23206,23209}       => {20725}  0.01037411 0.5931759   9.716042 226  
[121] {20725,23206}       => {23209}  0.01037411 0.5765306  12.349773 226  
[122] {20725,23209}       => {23206}  0.01037411 0.5368171  13.319545 226  
[123] {22383,23206}       => {20725}  0.01009869 0.6077348   9.954513 220  
[124] {20725,23206}       => {22383}  0.01009869 0.5612245  11.501670 220  
[125] {20726,22384}       => {20725}  0.01101675 0.7430341  12.170674 240  
[126] {20725,20726}       => {22384}  0.01101675 0.5466970  12.523444 240  
[127] {20726,20728}       => {20725}  0.01078724 0.6368564  10.431516 235  
[128] {20725,20726}       => {20728}  0.01078724 0.5353075  11.791379 235  
[129] {20725,20728}       => {20726}  0.01078724 0.5042918  13.094157 235  
[130] {20726,20727}       => {20725}  0.01064953 0.6590909  10.795711 232  
[131] {20725,20726}       => {20727}  0.01064953 0.5284738  10.729545 232  
[132] {20726,22382}       => {22383}  0.01005279 0.5354523  10.973498 219  
[133] {20726,22383}       => {22382}  0.01005279 0.5983607  13.022265 219  
[134] {20726,22382}       => {20725}  0.01193482 0.6356968  10.412523 260  
[135] {20725,20726}       => {22382}  0.01193482 0.5922551  12.889389 260  
[136] {20725,22382}       => {20726}  0.01193482 0.5531915  14.363858 260  
[137] {20726,22383}       => {20725}  0.01184301 0.7049180  11.546345 258  
[138] {20725,20726}       => {22383}  0.01184301 0.5876993  12.044242 258  
[139] {20728,22384}       => {20727}  0.01198072 0.6041667  12.266329 261  
[140] {20727,22384}       => {20728}  0.01198072 0.5588865  12.310761 261  
[141] {20727,20728}       => {22384}  0.01198072 0.6141176  14.067879 261  
[142] {20728,22384}       => {22382}  0.01032821 0.5208333  11.335019 225  
[143] {22382,22384}       => {20728}  0.01032821 0.6081081  13.394980 225  
[144] {20728,22382}       => {22384}  0.01032821 0.5696203  13.048556 225  
[145] {20728,22384}       => {22383}  0.01074134 0.5416667  11.100854 234  
[146] {22383,22384}       => {20728}  0.01074134 0.5792079  12.758387 234  
[147] {20728,22383}       => {22384}  0.01074134 0.5120350  11.729425 234  
[148] {20728,22384}       => {20725}  0.01294469 0.6527778  10.692304 282  
[149] {20725,22384}       => {20728}  0.01294469 0.5320755  11.720186 282  
[150] {20725,20728}       => {22384}  0.01294469 0.6051502  13.862458 282  
[151] {22382,22384}       => {20727}  0.01037411 0.6108108  12.401224 226  
[152] {20727,22382}       => {22384}  0.01037411 0.5393795  12.355817 226  
[153] {20727,22384}       => {22383}  0.01120037 0.5224839  10.707726 244  
[154] {22383,22384}       => {20727}  0.01120037 0.6039604  12.262141 244  
[155] {20727,22383}       => {22384}  0.01120037 0.5224839  11.968783 244  
[156] {20727,22384}       => {20725}  0.01418407 0.6616702  10.837959 309  
[157] {20725,22384}       => {20727}  0.01418407 0.5830189  11.836967 309  
[158] {20725,20727}       => {22384}  0.01418407 0.5908222  13.534239 309  
[159] {22382,22384}       => {20725}  0.01161350 0.6837838  11.200173 253  
[160] {20725,22382}       => {22384}  0.01161350 0.5382979  12.331040 253  
[161] {22383,22384}       => {20725}  0.01299059 0.7004950  11.473898 283  
[162] {20725,22384}       => {22383}  0.01299059 0.5339623  10.942961 283  
[163] {20725,22383}       => {22384}  0.01299059 0.5380228  12.324739 283  
[164] {20727,20728}       => {22382}  0.01028230 0.5270588  11.470506 224  
[165] {20728,22382}       => {20727}  0.01028230 0.5670886  11.513537 224  
[166] {20727,22382}       => {20728}  0.01028230 0.5346062  11.775931 224  
[167] {20727,20728}       => {22383}  0.01156759 0.5929412  12.151668 252  
[168] {20728,22383}       => {20727}  0.01156759 0.5514223  11.195466 252  
[169] {20727,22383}       => {20728}  0.01156759 0.5396146  11.886252 252  
[170] {20727,20728}       => {20725}  0.01207253 0.6188235  10.136143 263  
[171] {20725,20728}       => {20727}  0.01207253 0.5643777  11.458497 263  
[172] {20725,20727}       => {20728}  0.01207253 0.5028681  11.076826 263  
[173] {20728,22382}       => {22383}  0.01106266 0.6101266  12.503864 241  
[174] {20728,22383}       => {22382}  0.01106266 0.5273523  11.476893 241  
[175] {22382,22383}       => {20728}  0.01106266 0.5343681  11.770686 241  
[176] {20728,22382}       => {20725}  0.01087905 0.6000000   9.827820 237  
[177] {20725,20728}       => {22382}  0.01087905 0.5085837  11.068427 237  
[178] {20725,22382}       => {20728}  0.01087905 0.5042553  11.107383 237  
[179] {20728,22383}       => {20725}  0.01276107 0.6083151   9.964018 278  
[180] {20725,20728}       => {22383}  0.01276107 0.5965665  12.225966 278  
[181] {20725,22383}       => {20728}  0.01276107 0.5285171  11.641805 278  
[182] {20727,22382}       => {22383}  0.01156759 0.6014320  12.325678 252  
[183] {20727,22383}       => {22382}  0.01156759 0.5396146  11.743759 252  
[184] {22382,22383}       => {20727}  0.01156759 0.5587583  11.344408 252  
[185] {20727,22382}       => {20725}  0.01179711 0.6133652  10.046737 257  
[186] {20725,22382}       => {20727}  0.01179711 0.5468085  11.101793 257  
[187] {20727,22383}       => {20725}  0.01294469 0.6038544   9.890953 282  
[188] {20725,20727}       => {22383}  0.01294469 0.5391969  11.050240 282  
[189] {20725,22383}       => {20727}  0.01294469 0.5361217  10.884819 282  
[190] {22382,22383}       => {20725}  0.01253156 0.6053215   9.914984 273  
[191] {20725,22382}       => {22383}  0.01253156 0.5808511  11.903895 273  
[192] {20725,22383}       => {22382}  0.01253156 0.5190114  11.295368 273  
[193] {22697,22698,22699} => {22423}  0.01106266 0.6070529   7.023180 241  
[194] {22423,22697,22698} => {22699}  0.01106266 0.8795620  22.920166 241  
[195] {22423,22698,22699} => {22697}  0.01106266 0.8892989  26.430254 241  
[196] {22423,22697,22699} => {22698}  0.01106266 0.7626582  28.112537 241  
          lhs         rhs    support confidence     lift count
[1]   {21086} =>  {21094} 0.01083314  0.8280702 55.50618   236
[2]   {21094} =>  {21086} 0.01083314  0.7261538 55.50618   236
[7]   {23171} =>  {23170} 0.01064953  0.8436364 54.05476   232
[8]   {23170} =>  {23171} 0.01064953  0.6823529 54.05476   232
[5]   {23254} =>  {23256} 0.01005279  0.7711268 50.59939   219
[6]   {23256} =>  {23254} 0.01005279  0.6596386 50.59939   219
[23]  {22745} =>  {22748} 0.01170530  0.7993730 49.89783   255
[24]  {22748} =>  {22745} 0.01170530  0.7306590 49.89783   255
[31] {84596F} => {84596B} 0.01037411  0.7820069 47.06083   226
[32] {84596B} => {84596F} 0.01037411  0.6243094 47.06083   226

rules_df

top_10_rules

filtered_data <- subset(data, StockCode == "22748")
filtered_data$Description[1]

filtered_data <- subset(data, StockCode == "22745")
filtered_data$Description[1]

# Calculate Monetary value for each transaction if not already included
data$Monetary <- data$Quantity * data$UnitPrice

# Ensure InvoiceDate is a datetime type
data$InvoiceDate <- as.POSIXct(data$InvoiceDate, format = "%Y-%m-%d %H:%M:%S")

# Calculate Recency in days
current_date <- as.Date(max(data$InvoiceDate)) + 1
data$Recency <- as.numeric(difftime(current_date, data$InvoiceDate, units = "days"))

# Group by CustomerID to calculate RFM values per customer
rfm_df <- data %>%
  group_by(CustomerID) %>%
  summarize(
    Recency = min(Recency),             # Most recent purchase date for each customer
    Frequency = n_distinct(InvoiceNo),  # Number of unique transactions (counts of invoices) for frequency
    Monetary = sum(Monetary)            # Total sum for monetary value
  )

# Inspecting the first few rows of the RFM dataframe
head(rfm_df)

unique_customers <- data %>%
  distinct(CustomerID) %>%
  nrow()

print(unique_customers)

[1] 4362

dim(rfm_df)

head(rfm_df)

# Boxplot for Recency
ggplot(data = rfm_df, aes(x = "", y = Recency)) +
  geom_boxplot() +
  labs(title = "Boxplot of Recency")

# Boxplot for Frequency
ggplot(data = rfm_df, aes(x = "", y = Frequency)) +
  geom_boxplot() +
  labs(title = "Boxplot of Frequency")

# Boxplot for Monetary
ggplot(data = rfm_df, aes(x = "", y = Monetary)) +
  geom_boxplot() +
  labs(title = "Boxplot of Monetary")

# Remove the CustomerID column
new_df <- rfm_df[, c("Recency", "Frequency", "Monetary")]

# Remove outliers
new_df <- new_df[!apply(new_df, 1, function(x) any(abs(scale(x)) > 3)), ]

head(new_df)

# Select the columns
col_names <- c("Recency", "Frequency", "Monetary")
features <- new_df[, col_names]

# Scale the features
scaled_features <- scale(features)

# Convert the scaled features back to a dataframe
scaled_features <- as.data.frame(scaled_features)

# Rename the columns
colnames(scaled_features) <- col_names

head(scaled_features)

# Vector to store SSE values
SSE <- numeric(0)

# Loop through different numbers of clusters
for (cluster in 1:9) {
  # Fit KMeans model
  kmeans_model <- kmeans(scaled_features, centers = cluster, nstart = 10)
  
  # Calculate SSE and store it
  SSE <- c(SSE, kmeans_model$tot.withinss)
}

# Create a dataframe with Cluster and SSE values
frame <- data.frame(Cluster = 1:9, SSE = SSE)

# Plot SSE against number of clusters
plot(frame$Cluster, frame$SSE, type = "b", pch = 19, xlab = "Number of clusters", ylab = "Inertia")

k <- 4  # Number of clusters
# Fit the KMeans model
kmeans_model <- kmeans(scaled_features, centers = k, nstart = 25)

# Get cluster labels
cluster_labels <- kmeans_model$cluster

# Combine cluster labels with the original dataframe
rfm_df$Cluster <- cluster_labels

print(kmeans_model$centers)

     Recency   Frequency    Monetary
1 -0.7992213  2.43874285  1.18815081
2 -0.8648478 11.25028573 14.82165577
3  1.5570307 -0.35259309 -0.17376008
4 -0.4884870 -0.08048749 -0.07198866

head(rfm_df)

library(dplyr)
library(ggplot2)

# Group by cluster and calculate mean values for each feature
avg_df <- rfm_df %>%
  group_by(Cluster) %>%
  summarise(
    Recency = mean(Recency),
    Frequency = mean(Frequency),
    Monetary = mean(Monetary)
  )

avg_df

ggplot(avg_df, aes(x = as.factor(Cluster), y = Recency, fill = as.factor(Cluster))) +
  geom_bar(stat = "identity", color = "black") +
  labs(x = "Cluster", y = "Recency", title = "Average Recency by Cluster") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplot(avg_df, aes(x = as.factor(Cluster), y = Frequency, fill = as.factor(Cluster))) +
  geom_bar(stat = "identity", color = "black") +
  labs(x = "Cluster", y = "Frequency", title = "Average Frequency by Cluster") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplot(avg_df, aes(x = as.factor(Cluster), y = Monetary, fill = as.factor(Cluster))) +
  geom_bar(stat = "identity", color = "black") +
  labs(x = "Cluster", y = "Monetary", title = "Average Monetary by Cluster") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplot(rfm_df, aes(x = Recency, y = Frequency, z = Monetary, color = as.factor(Cluster))) +
  geom_point() +
  labs(title = "Scatter Plot of RFM Clusters (Individual)",
       x = "Recency", y = "Frequency", z = "Monetary") +
  theme_minimal()

head(rfm_df)

# Create scatter plot
ggplot(rfm_df, aes(x = Frequency, y = Monetary, color = as.factor(Cluster))) +
  geom_point() +
  labs(title = "Segmentation K-means",
       x = "Frequency", y = "Monetary") +
  scale_color_manual(values = c("green", "red", "cyan", "magenta")) +
  theme_minimal()

dim(rfm_df)

# Calculate the first and third quartiles
Q1 <- quantile(data$Quantity, 0.05)
Q3 <- quantile(data$Quantity, 0.95)

# Calculate the IQR
IQR <- Q3 - Q1

# Define the upper and lower bounds for outliers
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR

# Filter the data to remove outliers
filtered_data <- subset(data, Quantity >= lower_bound & Quantity <= upper_bound)

# Calculate the first and third quartiles
Q1 <- quantile(filtered_data$UnitPrice, 0.05)
Q3 <- quantile(filtered_data$UnitPrice, 0.95)

# Calculate the IQR
IQR <- Q3 - Q1

# Define the upper and lower bounds for outliers
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR

# Filter the data to remove outliers
filtered_data <- subset(filtered_data, UnitPrice >= lower_bound & UnitPrice <= upper_bound)

head(filtered_data)

dim(filtered_data)

# Distribution Analysis
print("\nDistribution Analysis:")
# Quantity Distribution
ggplot(filtered_data, aes(x = Quantity)) + 
  geom_histogram(binwidth = 10, fill = "skyblue", color = "black") +
  labs(title = "Quantity Distribution", x = "Quantity", y = "Frequency") +
  theme_minimal()

[1] "\nDistribution Analysis:"

ggplot(filtered_data, aes(x = UnitPrice)) + 
  geom_histogram(binwidth = 1, fill = "skyblue", color = "black") +
  labs(title = "UnitPrice Distribution", x = "UnitPrice", y = "Frequency") +
  theme_minimal()

print("\nTime Series Analysis:")
# Resample to monthly frequency
filtered_data$InvoiceDate <- as.Date(filtered_data$InvoiceDate)
monthly_sales <- filtered_data %>%
  group_by(month = floor_date(InvoiceDate, "month")) %>%
  summarize(total_quantity = sum(Quantity))

[1] "\nTime Series Analysis:"

ggplot(monthly_sales, aes(x = month, y = total_quantity)) +
  geom_line() +
  labs(title = "Monthly Sales Volume", x = "Month", y = "Total Quantity Sold") +
  theme_minimal()

# Customer Analysis
print("\nCustomer Analysis:")
# Top 10 customers by spend
top_customers <- filtered_data %>%
  group_by(CustomerID) %>%
  summarize(total_spend = sum(Quantity * UnitPrice)) %>%
  arrange(desc(total_spend)) %>%
  top_n(10)

[1] "\nCustomer Analysis:"

Selecting by total_spend

ggplot(top_customers, aes(x = reorder(CustomerID, -total_spend), y = total_spend)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Top 10 Customers by Spend", x = "Customer ID", y = "Total Spend") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

print("\nProduct Analysis:")
# Top 10 products by quantity sold
top_products <- filtered_data %>%
  group_by(Description) %>%
  summarize(total_quantity = sum(Quantity)) %>%
  arrange(desc(total_quantity)) %>%
  top_n(10)

ggplot(top_products, aes(x = reorder(Description, -total_quantity), y = total_quantity)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Top 10 Products by Quantity Sold", x = "Product Description", y = "Quantity Sold") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

[1] "\nProduct Analysis:"

Selecting by total_quantity

# Geographical Insights
print("\nGeographical Insights:")
# Sales by country
sales_by_country <- filtered_data %>%
  group_by(Country) %>%
  summarize(total_spend = sum(Quantity * UnitPrice)) %>%
  arrange(desc(total_spend))

ggplot(sales_by_country, aes(x = reorder(Country, -total_spend), y = total_spend)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Sales by Country", x = "Country", y = "Total Spend") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

[1] "\nGeographical Insights:"

# Sales Performance
print("\nSales Performance:")
# Monthly revenue
monthly_revenue <- filtered_data %>%
  group_by(month = floor_date(InvoiceDate, "month")) %>%
  summarize(total_revenue = sum(Quantity * UnitPrice))

ggplot(monthly_revenue, aes(x = month, y = total_revenue)) +
  geom_line() +
  labs(title = "Monthly Revenue", x = "Month", y = "Total Revenue") +
  theme_minimal()

[1] "\nSales Performance:"

# Order Analysis
print("\nOrder Analysis:")
# Number of items per order
items_per_order <- filtered_data %>%
  group_by(InvoiceNo) %>%
  summarize(total_quantity = sum(Quantity))

ggplot(items_per_order, aes(x = total_quantity)) +
  geom_histogram(binwidth = 10, fill = "skyblue", color = "black") +
  labs(title = "Distribution of Items per Order", x = "Items per Order", y = "Frequency") +
  theme_minimal()

[1] "\nOrder Analysis:"

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
142	C536379	D	Discount	-1	12/1/2010 9:41	27.50	14527	United Kingdom
155	C536383	35004C	SET OF 3 COLOURED FLYING DUCKS	-1	12/1/2010 9:49	4.65	15311	United Kingdom
236	C536391	22556	PLASTERS IN TIN CIRCUS PARADE	-12	12/1/2010 10:24	1.65	17548	United Kingdom
237	C536391	21984	PACK OF 12 PINK PAISLEY TISSUES	-24	12/1/2010 10:24	0.29	17548	United Kingdom
238	C536391	21983	PACK OF 12 BLUE PAISLEY TISSUES	-24	12/1/2010 10:24	0.29	17548	United Kingdom
239	C536391	21980	PACK OF 12 RED RETROSPOT TISSUES	-24	12/1/2010 10:24	0.29	17548	United Kingdom

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
518	536409	21866	UNION JACK FLAG LUGGAGE TAG	1	12/1/2010 11:45	1.25	17908	United Kingdom
528	536409	22866	HAND WARMER SCOTTY DOG DESIGN	1	12/1/2010 11:45	2.10	17908	United Kingdom
538	536409	22900	SET 2 TEA TOWELS I LOVE LONDON	1	12/1/2010 11:45	2.95	17908	United Kingdom
540	536409	22111	SCOTTIE DOG HOT WATER BOTTLE	1	12/1/2010 11:45	4.95	17908	United Kingdom
556	536412	22327	ROUND SNACK BOXES SET OF 4 SKULLS	1	12/1/2010 11:49	2.95	17920	United Kingdom
588	536412	22273	FELTCRAFT DOLL MOLLY	1	12/1/2010 11:49	2.95	17920	United Kingdom
590	536412	22749	FELTCRAFT PRINCESS CHARLOTTE DOLL	1	12/1/2010 11:49	3.75	17920	United Kingdom
595	536412	22141	CHRISTMAS CRAFT TREE TOP ANGEL	1	12/1/2010 11:49	2.10	17920	United Kingdom
599	536412	21448	12 DAISY PEGS IN WOOD BOX	1	12/1/2010 11:49	1.65	17920	United Kingdom
601	536412	22569	FELTCRAFT CUSHION BUTTERFLY	2	12/1/2010 11:49	3.75	17920	United Kingdom
602	536412	21448	12 DAISY PEGS IN WOOD BOX	2	12/1/2010 11:49	1.65	17920	United Kingdom
605	536412	21448	12 DAISY PEGS IN WOOD BOX	2	12/1/2010 11:49	1.65	17920	United Kingdom
606	536412	22902	TOTE BAG I LOVE LONDON	7	12/1/2010 11:49	2.10	17920	United Kingdom
617	536412	21708	FOLDING UMBRELLA CREAM POLKADOT	1	12/1/2010 11:49	4.95	17920	United Kingdom
618	536412	22900	SET 2 TEA TOWELS I LOVE LONDON	2	12/1/2010 11:49	2.95	17920	United Kingdom
619	536412	21706	FOLDING UMBRELLA RED/WHITE POLKADOT	1	12/1/2010 11:49	4.95	17920	United Kingdom
621	536412	85184C	S/4 VALENTINE DECOUPAGE HEART BOX	1	12/1/2010 11:49	2.95	17920	United Kingdom
759	536446	21651	HANGING GLASS ETCHED TEALIGHT	6	12/1/2010 12:15	1.65	15983	United Kingdom
832	536464	22866	HAND WARMER SCOTTY DOG DESIGN	1	12/1/2010 12:23	2.10	17968	United Kingdom
836	536464	22945	CHRISTMAS METAL TAGS ASSORTED	6	12/1/2010 12:23	0.85	17968	United Kingdom
853	536464	21992	VINTAGE PAISLEY STATIONERY SET	1	12/1/2010 12:23	2.95	17968	United Kingdom
860	536464	22866	HAND WARMER SCOTTY DOG DESIGN	1	12/1/2010 12:23	2.10	17968	United Kingdom
892	536488	84347	ROTATING SILVER ANGELS T-LIGHT HLDR	1	12/1/2010 12:31	2.55	17897	United Kingdom
1062	536522	21121	SET/10 RED POLKADOT PARTY CANDLES	1	12/1/2010 12:49	1.25	15012	United Kingdom
1068	536522	21122	SET/10 PINK POLKADOT PARTY CANDLES	1	12/1/2010 12:49	1.25	15012	United Kingdom
1141	536528	22865	HAND WARMER OWL DESIGN	1	12/1/2010 13:17	2.10	15525	United Kingdom
1154	536528	85114B	IVORY ENCHANTED FOREST PLACEMAT	1	12/1/2010 13:17	1.65	15525	United Kingdom
1160	536528	22584	PACK OF 6 PANNETONE GIFT BOXES	2	12/1/2010 13:17	2.55	15525	United Kingdom
1166	536528	22911	PAPER CHAIN KIT LONDON	1	12/1/2010 13:17	2.95	15525	United Kingdom
1167	536528	22743	MAKE YOUR OWN FLOWERPOWER CARD KIT	1	12/1/2010 13:17	2.95	15525	United Kingdom
...	...	...	...	...	...	...	...	...
537818	581352	22481	BLACK TEA TOWEL CLASSIC DESIGN	2	12/8/2011 12:26	0.39	14698	United Kingdom
537820	581352	82494L	WOODEN FRAME ANTIQUE WHITE	1	12/8/2011 12:26	2.95	14698	United Kingdom
537825	581352	22564	ALPHABET STENCIL CRAFT	1	12/8/2011 12:26	1.25	14698	United Kingdom
538295	581404	22469	HEART OF WICKER SMALL	2	12/8/2011 13:47	1.65	13680	United Kingdom
538377	581405	22500	SET OF 2 TINS JARDIN DE PROVENCE	1	12/8/2011 13:50	1.25	13521	United Kingdom
538467	581405	20975	12 PENCILS SMALL TUBE RED RETROSPOT	1	12/8/2011 13:50	0.65	13521	United Kingdom
538495	581405	23212	HEART WREATH DECORATION WITH BELL	1	12/8/2011 13:50	1.25	13521	United Kingdom
538627	581412	21481	FAWN BLUE HOT WATER BOTTLE	1	12/8/2011 14:38	3.75	14415	United Kingdom
538678	581412	22199	FRYING PAN RED RETROSPOT	1	12/8/2011 14:38	1.25	14415	United Kingdom
538752	581414	22326	ROUND SNACK BOXES SET OF4 WOODLAND	1	12/8/2011 14:39	2.95	14730	United Kingdom
538757	581414	22094	RED RETROSPOT TISSUE BOX	1	12/8/2011 14:39	0.39	14730	United Kingdom
538763	581414	22379	RECYCLING BAG RETROSPOT	1	12/8/2011 14:39	2.10	14730	United Kingdom
538772	581414	23291	DOLLY GIRL CHILDRENS CUP	1	12/8/2011 14:39	1.25	14730	United Kingdom
538777	581414	22327	ROUND SNACK BOXES SET OF 4 SKULLS	1	12/8/2011 14:39	2.95	14730	United Kingdom
538782	581414	22327	ROUND SNACK BOXES SET OF 4 SKULLS	1	12/8/2011 14:39	2.95	14730	United Kingdom
538786	581414	23454	THREE MINI HANGING FRAMES	1	12/8/2011 14:39	4.15	14730	United Kingdom
538943	581425	85152	HAND OVER THE CHOCOLATE SIGN	12	12/8/2011 15:31	2.10	14796	United Kingdom
538956	581425	21231	SWEETHEART CERAMIC TRINKET BOX	4	12/8/2011 15:31	1.25	14796	United Kingdom
538971	581425	23178	JAM CLOCK MAGNET	1	12/8/2011 15:31	1.25	14796	United Kingdom
539893	581449	22423	REGENCY CAKESTAND 3 TIER	1	12/8/2011 17:37	12.75	12748	United Kingdom
539927	581450	22118	JOY WOODEN BLOCK LETTERS	1	12/8/2011 17:54	1.25	16794	United Kingdom
540033	581456	35964	FOLKART CLIP ON STARS	2	12/8/2011 18:42	0.39	17530	United Kingdom
540199	581471	21411	GINGHAM HEART DOORSTOP RED	2	12/8/2011 19:29	1.95	14702	United Kingdom
541612	581514	22075	6 RIBBONS ELEGANT CHRISTMAS	24	12/9/2011 11:20	0.39	17754	United Kingdom
541656	581538	23275	SET OF 3 HANGING OWLS OLLIE BEAK	1	12/9/2011 11:34	1.25	14446	United Kingdom
541676	581538	22068	BLACK PIRATE TREASURE CHEST	1	12/9/2011 11:34	0.39	14446	United Kingdom
541690	581538	23318	BOX OF 6 MINI VINTAGE CRACKERS	1	12/9/2011 11:34	2.49	14446	United Kingdom
541693	581538	22992	REVOLVER WOODEN RULER	1	12/9/2011 11:34	1.95	14446	United Kingdom
541700	581538	22694	WICKER STAR	1	12/9/2011 11:34	2.10	14446	United Kingdom
541702	581538	23343	JUMBO BAG VINTAGE CHRISTMAS	1	12/9/2011 11:34	2.08	14446	United Kingdom

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country	is_Cancelled	len_StockCode
46	536370	POST	POSTAGE	3	12/1/2010 8:45	18.00	12583	France	FALSE	4
142	C536379	D	Discount	-1	12/1/2010 9:41	27.50	14527	United Kingdom	TRUE	1
387	536403	POST	POSTAGE	1	12/1/2010 11:27	15.00	12791	Netherlands	FALSE	4
1098	536527	POST	POSTAGE	1	12/1/2010 13:04	18.00	12662	Germany	FALSE	4
1387	536540	C2	CARRIAGE	1	12/1/2010 14:05	50.00	14911	EIRE	FALSE	2
1655	536569	M	Manual	1	12/1/2010 15:35	1.25	16274	United Kingdom	FALSE	1
1666	536569	M	Manual	1	12/1/2010 15:35	18.95	16274	United Kingdom	FALSE	1
3790	536840	POST	POSTAGE	1	12/2/2010 18:27	18.00	12738	Germany	FALSE	4
3969	536852	POST	POSTAGE	1	12/3/2010 9:51	18.00	12686	France	FALSE	4
4036	536858	POST	POSTAGE	2	12/3/2010 10:36	40.00	13520	Switzerland	FALSE	4
4080	536861	POST	POSTAGE	3	12/3/2010 10:44	18.00	12427	Germany	FALSE	4
4411	536967	POST	POSTAGE	1	12/3/2010 12:57	18.00	12600	Germany	FALSE	4
4485	536974	POST	POSTAGE	2	12/3/2010 13:59	18.00	12682	France	FALSE	4
4607	536981	M	Manual	2	12/3/2010 14:26	0.85	14723	United Kingdom	FALSE	1
4636	536983	POST	POSTAGE	1	12/3/2010 14:30	18.00	12712	Germany	FALSE	4
4802	536990	POST	POSTAGE	1	12/3/2010 15:14	28.00	12793	Portugal	FALSE	4
4865	537022	POST	POSTAGE	2	12/3/2010 15:45	28.00	12725	Italy	FALSE	4
4903	537026	POST	POSTAGE	2	12/3/2010 16:35	18.00	12395	Belgium	FALSE	4
5550	537065	POST	POSTAGE	9	12/5/2010 11:57	18.00	12567	France	FALSE	4
5573	537077	M	Manual	12	12/5/2010 11:59	0.42	17062	United Kingdom	FALSE	1
6116	537137	M	Manual	36	12/5/2010 12:43	0.85	16327	United Kingdom	FALSE	1
6173	537140	M	Manual	1	12/5/2010 12:53	0.42	12748	United Kingdom	FALSE	1
6174	537140	M	Manual	1	12/5/2010 12:53	0.85	12748	United Kingdom	FALSE	1
6597	C537164	D	Discount	-1	12/5/2010 13:21	29.29	14527	United Kingdom	TRUE	1
6841	537197	POST	POSTAGE	3	12/5/2010 14:02	18.00	12647	Germany	FALSE	4
6846	537198	POST	POSTAGE	1	12/5/2010 14:03	18.00	12647	Germany	FALSE	4
6866	537199	M	Manual	1	12/5/2010 14:06	0.65	15894	United Kingdom	FALSE	1
6977	537201	POST	POSTAGE	6	12/5/2010 14:19	18.00	12472	Germany	FALSE	4
7105	537208	M	Manual	4	12/5/2010 15:12	0.85	15889	United Kingdom	FALSE	1
7257	537212	POST	POSTAGE	4	12/5/2010 15:21	18.00	12720	Germany	FALSE	4
...	...	...	...	...	...	...	...	...	...	...
393745	580736	POST	POSTAGE	2	12/6/2011 8:55	18.00	12716	France	FALSE	4
393855	580752	POST	POSTAGE	3	12/6/2011 9:56	45.00	12478	Greece	FALSE	4
393922	580753	POST	POSTAGE	3	12/6/2011 10:00	18.00	12682	France	FALSE	4
393929	580756	POST	POSTAGE	1	12/6/2011 10:19	18.00	12723	France	FALSE	4
394903	580884	M	Manual	1	12/6/2011 12:21	0.85	15907	United Kingdom	FALSE	1
395434	580955	POST	POSTAGE	1	12/6/2011 14:22	28.00	12442	Spain	FALSE	4
395435	580956	M	Manual	4	12/6/2011 14:23	1.25	17841	United Kingdom	FALSE	1
395619	C580957	POST	POSTAGE	-1	12/6/2011 14:23	4.50	12839	United Kingdom	TRUE	4
395773	580965	POST	POSTAGE	3	12/6/2011 14:52	15.00	12417	Belgium	FALSE	4
395919	580979	POST	POSTAGE	3	12/6/2011 15:40	18.00	12362	Belgium	FALSE	4
396074	580986	POST	POSTAGE	4	12/6/2011 16:34	18.00	12650	France	FALSE	4
396276	581000	POST	POSTAGE	5	12/7/2011 8:03	18.00	12720	Germany	FALSE	4
396288	581001	POST	POSTAGE	3	12/7/2011 8:07	18.00	12583	France	FALSE	4
396441	C581009	M	Manual	-1	12/7/2011 9:15	125.00	16971	United Kingdom	TRUE	1
397436	C581145	M	Manual	-1	12/7/2011 13:48	9.95	17490	United Kingdom	TRUE	1
397917	581171	POST	POSTAGE	2	12/7/2011 15:02	18.00	12615	France	FALSE	4
398114	581179	POST	POSTAGE	1	12/7/2011 15:43	240.00	12471	Germany	FALSE	4
398159	581182	POST	POSTAGE	4	12/7/2011 15:56	28.00	12783	Portugal	FALSE	4
398170	581183	POST	POSTAGE	4	12/7/2011 16:24	18.00	12569	Germany	FALSE	4
398174	581184	POST	POSTAGE	2	12/7/2011 16:24	18.00	12569	Germany	FALSE	4
398627	581232	POST	POSTAGE	4	12/8/2011 10:26	40.00	12358	Austria	FALSE	4
398846	581266	POST	POSTAGE	5	12/8/2011 11:25	18.00	12621	Germany	FALSE	4
398849	581279	POST	POSTAGE	3	12/8/2011 11:35	18.00	12437	France	FALSE	4
399699	581405	M	Manual	3	12/8/2011 13:50	0.42	13521	United Kingdom	FALSE	1
401178	581493	POST	POSTAGE	1	12/9/2011 10:10	15.00	12423	Belgium	FALSE	4
401196	581494	POST	POSTAGE	2	12/9/2011 10:13	18.00	12518	Germany	FALSE	4
401244	C581499	M	Manual	-1	12/9/2011 10:28	224.69	15498	United Kingdom	TRUE	1
401426	581570	POST	POSTAGE	1	12/9/2011 11:59	18.00	12662	Germany	FALSE	4
401463	581574	POST	POSTAGE	2	12/9/2011 12:09	18.00	12526	Germany	FALSE	4
401464	581578	POST	POSTAGE	3	12/9/2011 12:16	18.00	12713	Germany	FALSE	4

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	CustomerID	Country	is_Cancelled	len_StockCode
6843	537197	22841	ROUND CAKE TIN VINTAGE GREEN	1	12/5/2010 14:02	12647	Germany	FALSE	5
22620	539263	22580	ADVENT CALENDAR GINGHAM SACK	4	12/16/2010 14:36	16560	United Kingdom	FALSE	5
25552	539722	22423	REGENCY CAKESTAND 3 TIER	10	12/21/2010 13:45	14911	EIRE	FALSE	5
29375	540372	22090	PAPER BUNTING RETROSPOT	24	1/6/2011 16:41	13081	United Kingdom	FALSE	5
29377	540372	22553	PLASTERS IN TIN SKULLS	24	1/6/2011 16:41	13081	United Kingdom	FALSE	5
34904	541109	22168	ORGANISER WOOD ANTIQUE WHITE	1	1/13/2011 15:10	15107	United Kingdom	FALSE	5

InvoiceDate	Sales
2010-12-01	45737.56
2010-12-02	42547.42
2010-12-03	25283.73
2010-12-05	30543.61
2010-12-06	30061.75
2010-12-07	53126.64
2010-12-08	37809.22
2010-12-09	35114.58
2010-12-10	32759.73
2010-12-12	17014.20
2010-12-13	27223.84
2010-12-14	26789.23
2010-12-15	29324.76
2010-12-16	43416.13
2010-12-17	21228.41
2010-12-18	33.00
2010-12-19	7153.35
2010-12-20	17334.56
2010-12-21	15415.71
2010-12-22	4776.12
2010-12-23	5200.37
2011-01-04	10858.36
2011-01-05	28074.22
2011-01-06	31704.85
2011-01-07	22942.80
2011-01-09	15334.53
2011-01-10	14763.70
2011-01-11	59042.51
2011-01-12	16133.55
2011-01-13	14782.36
...	...
2011-11-06	42114.91
2011-11-07	26475.81
2011-11-08	36916.09
2011-11-09	57275.99
2011-11-10	62231.47
2011-11-11	41158.03
2011-11-13	27879.22
2011-11-14	55826.58
2011-11-15	46076.27
2011-11-16	46215.65
2011-11-17	50402.72
2011-11-18	38015.95
2011-11-20	29289.83
2011-11-21	43374.72
2011-11-22	47950.01
2011-11-23	69390.52
2011-11-24	33330.62
2011-11-25	27200.94
2011-11-27	16923.39
2011-11-28	49077.83
2011-11-29	47541.59
2011-11-30	40094.66
2011-12-01	40158.47
2011-12-02	46765.47
2011-12-04	19943.60
2011-12-05	55332.24
2011-12-06	42984.87
2011-12-07	68347.61
2011-12-08	33495.22
2011-12-09	31124.54

E-Commerce Retail Store Data Analysis¶

Data Pre processing¶

Missing values calculation¶

Summary statistics¶

The proportions of transactions originating from each country in the dataset, providing insights into the geographical distribution of sales.¶

Convert "Description" to character type and remove extra whitespaces¶

Remove missing values¶

Identifying and removing duplicates¶

Exploratory Data Analysis¶

Feature engineering¶

Zero unit prices may indicate free items or data entry errors. To understand their nature, it is essential to investigate these zero unit price transactions further.¶

Time Series Analysis¶

Pareto Principle Analysis¶

27% of Customers contribute to 80% of sales.¶

22% of Products contribute to 80% of sales.¶

Market Basket Analysis¶

Example:¶

{22745} => {22748} support: 0.01170530 confidence: 0.7993730 lift: 49.89783 count: 255¶

Explanation:¶

lhs: {22745} (left-hand side) indicates the item "22745"¶

¶

.¶

RMF Analysis¶

Customer Segmentation - K means Clustering¶

InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	12/1/2010 8:26	2.55	17850	United Kingdom
536365	71053	WHITE METAL LANTERN	6	12/1/2010 8:26	3.39	17850	United Kingdom
536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	12/1/2010 8:26	2.75	17850	United Kingdom
536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	12/1/2010 8:26	3.39	17850	United Kingdom
536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	12/1/2010 8:26	3.39	17850	United Kingdom
536365	22752	SET 7 BABUSHKA NESTING BOXES	2	12/1/2010 8:26	7.65	17850	United Kingdom

YearMonth	Sales
2010-12	547893.9
2011-01	471430.3
2011-02	434018.2
2011-03	572153.2
2011-04	422112.7
2011-05	650314.2
2011-06	639609.2
2011-07	581234.8
2011-08	612605.1
2011-09	921808.7
2011-10	961042.5
2011-11	1113102.1
2011-12	338152.0

CustomerID	Total_Value
12346	0.00
12347	4310.00
12348	1437.24
12349	1457.55
12350	294.40
12352	1265.41

CustomerID	Total_Value
14646	278778.0
18102	259657.3
17450	189575.5
14911	128768.2
12415	123638.2
14156	113685.8

CustomerID	Total_Value	Cumulative_Percentage
14646	278778.0	3.372800
18102	259657.3	6.514268
17450	189575.5	8.807850
14911	128768.2	10.365755
12415	123638.2	11.861593
14156	113685.8	13.237022

StockCode	Total_Value	Cumulative_Percentage
22423	132567.70	1.603872
85123A	93923.15	2.740203
85099B	83056.52	3.745064
47566	67628.43	4.563267
84879	56331.91	5.244800
23084	51042.84	5.862342

	lhs		rhs	support	confidence	lift	count
[1]	{21086}	=>	{21094}	0.01083314	0.8280702	55.506181	236
[2]	{21094}	=>	{21086}	0.01083314	0.7261538	55.506181	236
[3]	{84997C}	=>	{84997D}	0.01051182	0.7387097	37.688034	229
[4]	{84997D}	=>	{84997C}	0.01051182	0.5362998	37.688034	229
[5]	{23254}	=>	{23256}	0.01005279	0.7711268	50.599387	219
[6]	{23256}	=>	{23254}	0.01005279	0.6596386	50.599387	219
[7]	{23171}	=>	{23170}	0.01064953	0.8436364	54.054759	232
[8]	{23170}	=>	{23171}	0.01064953	0.6823529	54.054759	232
[9]	{47590B}	=>	{47590A}	0.01175120	0.7052342	42.914878	256
[10]	{47590A}	=>	{47590B}	0.01175120	0.7150838	42.914878	256
[11]	{22725}	=>	{22727}	0.01055772	0.6865672	16.490480	230
[12]	{21136}	=>	{84879}	0.01175120	0.7231638	11.374819	256
[13]	{22729}	=>	{22726}	0.01028230	0.5957447	16.042395	224
[14]	{22729}	=>	{22727}	0.01165940	0.6755319	16.225428	254
[15]	{23295}	=>	{23293}	0.01133808	0.6785714	31.452508	247
[16]	{23293}	=>	{23295}	0.01133808	0.5255319	31.452508	247
[17]	{22579}	=>	{22578}	0.01042001	0.8194946	40.946536	227
[18]	{22578}	=>	{22579}	0.01042001	0.5206422	40.946536	227
[19]	{21094}	=>	{21080}	0.01051182	0.7046154	20.912869	229
[20]	{82581}	=>	{82580}	0.01042001	0.7394137	41.515791	227
[21]	{82580}	=>	{82581}	0.01042001	0.5850515	41.515791	227
[22]	{23243}	=>	{22720}	0.01009869	0.5250597	9.391153	220
[23]	{22745}	=>	{22748}	0.01170530	0.7993730	49.897827	255
[24]	{22748}	=>	{22745}	0.01170530	0.7306590	49.897827	255
[25]	{22625}	=>	{22624}	0.01239385	0.5793991	20.067107	270
[26]	{23343}	=>	{23344}	0.01133808	0.6301020	23.148015	247
[27]	{22749}	=>	{22750}	0.01092495	0.5935162	34.025660	238
[28]	{22750}	=>	{22749}	0.01092495	0.6263158	34.025660	238
[29]	{22730}	=>	{22726}	0.01266927	0.5750000	15.483776	276
[30]	{22730}	=>	{22727}	0.01459720	0.6625000	15.912417	318
...	...	...	...	...	...	...	...
[167]	{20727,20728}	=>	{22383}	0.01156759	0.5929412	12.151668	252
[168]	{20728,22383}	=>	{20727}	0.01156759	0.5514223	11.195466	252
[169]	{20727,22383}	=>	{20728}	0.01156759	0.5396146	11.886252	252
[170]	{20727,20728}	=>	{20725}	0.01207253	0.6188235	10.136143	263
[171]	{20725,20728}	=>	{20727}	0.01207253	0.5643777	11.458497	263
[172]	{20725,20727}	=>	{20728}	0.01207253	0.5028681	11.076826	263
[173]	{20728,22382}	=>	{22383}	0.01106266	0.6101266	12.503864	241
[174]	{20728,22383}	=>	{22382}	0.01106266	0.5273523	11.476893	241
[175]	{22382,22383}	=>	{20728}	0.01106266	0.5343681	11.770686	241
[176]	{20728,22382}	=>	{20725}	0.01087905	0.6000000	9.827820	237
[177]	{20725,20728}	=>	{22382}	0.01087905	0.5085837	11.068427	237
[178]	{20725,22382}	=>	{20728}	0.01087905	0.5042553	11.107383	237
[179]	{20728,22383}	=>	{20725}	0.01276107	0.6083151	9.964018	278
[180]	{20725,20728}	=>	{22383}	0.01276107	0.5965665	12.225966	278
[181]	{20725,22383}	=>	{20728}	0.01276107	0.5285171	11.641805	278
[182]	{20727,22382}	=>	{22383}	0.01156759	0.6014320	12.325678	252
[183]	{20727,22383}	=>	{22382}	0.01156759	0.5396146	11.743759	252
[184]	{22382,22383}	=>	{20727}	0.01156759	0.5587583	11.344408	252
[185]	{20727,22382}	=>	{20725}	0.01179711	0.6133652	10.046737	257
[186]	{20725,22382}	=>	{20727}	0.01179711	0.5468085	11.101793	257
[187]	{20727,22383}	=>	{20725}	0.01294469	0.6038544	9.890953	282
[188]	{20725,20727}	=>	{22383}	0.01294469	0.5391969	11.050240	282
[189]	{20725,22383}	=>	{20727}	0.01294469	0.5361217	10.884819	282
[190]	{22382,22383}	=>	{20725}	0.01253156	0.6053215	9.914984	273
[191]	{20725,22382}	=>	{22383}	0.01253156	0.5808511	11.903895	273
[192]	{20725,22383}	=>	{22382}	0.01253156	0.5190114	11.295368	273
[193]	{22697,22698,22699}	=>	{22423}	0.01106266	0.6070529	7.023180	241
[194]	{22423,22697,22698}	=>	{22699}	0.01106266	0.8795620	22.920166	241
[195]	{22423,22698,22699}	=>	{22697}	0.01106266	0.8892989	26.430254	241
[196]	{22423,22697,22699}	=>	{22698}	0.01106266	0.7626582	28.112537	241

Recency	Frequency	Monetary
2.3105470	-0.3296525	-0.22923618
-0.8882559	0.2208200	0.29217240
-0.1653067	-0.1094635	-0.05536396
-0.7298013	-0.4397470	-0.05290693
2.1619958	-0.4397470	-0.19362071
-0.5515398	0.3309144	-0.07615135

Cluster	Recency	Frequency	Monetary
1	11.990291	27.145631	11716.2205
2	5.363636	107.181818	124411.7309
3	249.913488	1.791628	458.5693
4	43.366775	4.263192	1299.8190